Medical imaging techniques, such as computed topography (CT) and X-ray imaging, are widely used in diagnosis, clinical studies and treatment planning. There is an emerging need for automated approaches to improve the efficiency, accuracy and cost effectiveness of the medical imaging evaluation.
Non-contrast head CT scans are among the most commonly used emergency room diagnostic tools for patients with head injury or in those with symptoms suggesting a stroke or rise in intracranial pressure. Their wide availability and relatively low acquisition time makes them a commonly used first-line diagnostic modality. The percentage of annual US emergency room visits that involve a CT scan has been increasing for the last few decades and the use of head CT to exclude the need for neurosurgical intervention is on the rise.
The most critical, time-sensitive abnormalities that can be readily detected on CT scan include intracranial hemorrhages, raised intracranial pressure and cranial fractures. A key evaluation goal in patients with stroke is excluding an intracranial hemorrhage. This depends on CT imaging and its swift interpretation. Similarly, immediate CT scan interpretation is crucial in patients with a suspected acute intracranial hemorrhage to evaluate the need for neurosurgical treatment. Cranial fractures, if open or depressed will usually require urgent neurosurgical intervention. Cranial fractures are also the most commonly missed major abnormality on head CT scans, especially if coursing in an axial plane.
While these abnormalities are found only on a small fraction of CT scans, streamlining the head CT scan interpretation workflow by automating the initial screening and triage process, would significantly decrease the time to diagnosis and expedite treatment. This would in turn decrease morbidity and mortality consequent to stroke and head injury. An automated head CT scan screening and triage system would be valuable for queue management in a busy trauma care setting, or to facilitate decision-making in remote locations without an immediate radiologist availability.
The past year has seen a number of advances in application of deep learning for medical imaging interpretation tasks, with robust evidence that deep learning can perform specific medical imaging tasks including identifying and grading diabetic retinopathy and classifying skin lesions as benign or malignant with accuracy equivalent to specialist physicians. Deep learning algorithms have also been trained to detect abnormalities on radiological images such as chest radiographs, chest CT and head CT through ‘classification’ algorithms; as well as to localize and quantify disease patterns or anatomical volumes through ‘segmentation’ algorithms.
The development of an accurate deep learning algorithm for radiology requires, in addition to appropriate model architectures, a large number of accurately labeled scans that will be used to train the algorithm. The chances that the algorithm generalizes well to new settings increase when the training dataset is large and includes scans from diverse sources.
There are several studies on development and validation of Computer aided diagnosis (CAD) algorithms on low volumes of head CT scans. Deep learning has been earlier used to detect intracranial hemorrhages. Traditional computer vision techniques were more common for detection of fractures and midline shift. Training and validation datasets had <200 head CT scans for most studies, raising concerns about the robustness of these algorithms. Furthermore, there were no standard public head CT datasets to directly compare the algorithms' performance.